AI Solution to Assist Online Education Productivity via Personalizing Learning Strategies and Analyzing the Student Performance

Liyanage, Mohan; Hirimuthugoda, U.J; Liyanage, Nadun; Thammita, D.H.M.M.P; Wedanage, Dinindu Koliya Harshanath Webadu; Kugathasan, Archchana; Thelijjagoda, Samantha

doi:10.1109/uemcon54665.2022.9965735

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Interaction and real-time feedback [12]: Large models can also provide interactive learning experiences and real-time feedback, which is particularly important for deepening students' learning understanding. By interacting with the big model, students can immediately understand the effectiveness of their answers and solutions, and make adjustments based on the feedback provided by the model [17]. This rapid iterative learning process helps students gain a deeper understanding of complex issues in logistics management.…”

Section: Increase Teaching Depthmentioning

confidence: 99%

“…Adaptive learning path: Traditional teaching methods find it difficult to provide immediate and personalized feedback on the specific progress of each student, which may lead to students not being able to receive timely help when facing difficulties [17]. The large model can dynamically adjust the teaching content and difficulty, and adjust the curriculum in a timely manner based on student progress and feedback [21].…”

Section: Increase Teaching Depthmentioning

confidence: 99%

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Exploration and Practice of the Application of Large Models in Undergraduate Course Teaching of Economics and Management——"Introduction to Logistics Management" as an Example

2024

IJECMR

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With the rapid development of information technology and the continuous growth of the logistics industry, logistics management teaching is also facing new challenges and opportunities. Traditional logistics management courses often focus on imparting theoretical knowledge, but lack the cultivation of practical problem-solving abilities. The introduction of big model technology provides new ideas and methods for logistics management teaching. By simulating real logistics scenarios, students can master the core concepts and methods of logistics management in practice. This article explores the significance and value of the application of big models in logistics management teaching from three aspects: breadth, depth, and timeliness of the impact of the introduction of big models on logistics management teaching. It also analyzes the specific application of big models in logistics management teaching through practical cases, in order to provide reference and inspiration for teaching practice in related fields.

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Section: Increase Teaching Depthmentioning

confidence: 99%

Section: Increase Teaching Depthmentioning

confidence: 99%

Exploration and Practice of the Application of Large Models in Undergraduate Course Teaching of Economics and Management——"Introduction to Logistics Management" as an Example

2024

IJECMR

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Adaptive learning in programming education: A systematic mapping of the literature

Mota Vieira,

Magalhães Netto

2023

2023 IEEE Frontiers in Education Conference (FIE)

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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

Gligorea,

Cioca,

Oancea

et al. 2023

Education Sciences

122

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The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, retention, and performance. A comprehensive literature review was conducted, focusing on articles published from 2010 onwards, to document the integration of AI/ML in e-learning. The review analyzed 63 articles, employing a systematic approach to evaluate the deployment of adaptive learning algorithms and their educational implications. Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. These technologies have been shown to optimize learning paths, enhance engagement, and improve academic performance, with some studies reporting increased test scores. The integration of AI/ML in e-learning platforms significantly contributes to the personalization and effectiveness of the educational process. Despite challenges like data privacy and the complexity of AI/ML systems, the results underscore the potential of adaptive learning to revolutionize education by catering to individual learner needs.

show abstract

AI Solution to Assist Online Education Productivity via Personalizing Learning Strategies and Analyzing the Student Performance

Cited by 4 publications

References 11 publications

Exploration and Practice of the Application of Large Models in Undergraduate Course Teaching of Economics and Management——"Introduction to Logistics Management" as an Example

Exploration and Practice of the Application of Large Models in Undergraduate Course Teaching of Economics and Management——"Introduction to Logistics Management" as an Example

Adaptive learning in programming education: A systematic mapping of the literature

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

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